Hydrogen-1 magnetic resonance spectroscopy
(1H-MRS) allows noninvasive in vivo quantification of metabolite concentrations in brain tissue. In this work two of the most aggressive brain tumors are studied with the purpose ...

The Facioscapulohumeral Muscular Dystrophy (FSHD) is an autosomal dominant neuromuscular disorder whose incidence is estimated in about one in 400,000 to one in 20,000. No effective therapeutic strategies are known to halt ...

Machine learning is a powerful paradigm within which to analyze 1HMRS spectral data for the classification of tumour pathologies. An important characteristic
of this task is the high dimensionality of the involved data ...

In this work a new way to calculate the multivariate joint entropy is presented. This measure is the basis for a fast information-theoretic based evaluation of gene relevance in a Microarray Gene Expression data context. ...

In cancer diagnosis, classification of the different tumor types is of great importance. An accurate prediction of basic tumor types provides better treatment and may minimize the negative impact of incorrectly targeted ...

In cancer diagnosis, classification of the different tumor types is of great importance. An accurate prediction of different tumor types provides better treatment and may minimize the negative impact of incorrectly targeted ...

Facioscapulohumeral muscular dystrophy (FSHD) is a neuromuscular disorder that shows a preference for the facial, shoulder and upper arm muscles. FSHD affects about one in 20-400,000 people, and no effective therapeutic ...

Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides ...

Machine learning is a powerful paradigm within which to analyze 1H-MRS spectral data for the automated classi¯cation of tumor
pathologies aimed to facilitate clinical diagnosis. The high dimensionality of the involved ...

Machine Learning methods have of late made significant efforts to solving multidisciplinary problems in the field of cancer classification in microarray gene expression data. These tasks are characterized by a large number ...

Machine learning is a powerful paradigm to analyze Proton Magnetic Resonance Spectroscopy (1H-MRS) spectral data for the classification of brain tumor pathologies. An important characteristic of this task is the high ...

Machine learning is a powerful paradigm to analyze Proton Magnetic Resonance Spectroscopy (1H-MRS) spectral data for the classification of brain tumor pathologies. An
important characteristic of this task is the high ...